• Title/Summary/Keyword: statistical process control chart

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Statistical Process Control Software developed by MS-EXCEL and Visual Basic (MS-EXCEL과 Visual Basic으로 개발한 통계적 공정관리 소프트웨어)

  • Han, Kyung-Soo;Ahn, Jeong-Yong
    • Journal of Korean Society for Quality Management
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    • v.24 no.2
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    • pp.172-178
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    • 1996
  • In this study, we developed a software for statistical process control. This software presents $\bar{x}$, R, CUSUM, EWMA control chart and process capability index. In this system, statistical process control methods are integrated into the automated method on a real time base. It is available in process control of specified type and can be performed on personal computer with network system.

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Applying an Expert System to Statistical Process Control (통계적 공정 제어에 전문가 시스템의 적용에 관한 연구)

  • 윤건상;김훈모;최문규
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1995.10a
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    • pp.411-414
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    • 1995
  • Statistical Process Control (SPC) is a set of methodologies for signaling the presence of undesired sources of variation in manufacturing processes. Expert System in SPC can serve as a valuable tool to automate the analysis and interpretation of control charts. In this paper we put forward a method of successful application of Expert System to SPC in manufacturing process.

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Statistical Process Control and Adjustment using Process Incapability Index (공정비능력지수를 이용한 통계적 공정관리와 조정)

  • 구본철
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.24 no.63
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    • pp.45-54
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    • 2001
  • The process capability indices have been widely used in manufacturing industries to provide numerical measures of process potential and performance. This study is concerned with process controls and adjustments by incapability index $C_{pp}$ and its sub-indices. A monitoring for $\^{C}_{pp}$ would provide a convenient way to monitor changes on process capability after statistical control is established, since $C_{pp}$ simultaneously measures process variability and centering. Further, we can separate charting of process location and variability by sub-indices of $C_{pp}$, ($C_{ia}$, $C_{ip}$), without returning to $\={x}$-R chart, even though an out-of-control signals on $\^{C}_{pp}$ control chart is found.

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CUSUM of Squares Chart for the Detection of Variance Change in the Process

  • Lee, Jeong-Hyeong;Cho, Sin-Sup;Kim, Jae-Joo
    • Journal of Korean Society for Quality Management
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    • v.26 no.1
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    • pp.126-142
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    • 1998
  • Traditional statistical process control(SPC) assumes that consective observations from a process are independent. In industrial practice, however, observations are ofter serially correlated. A common a, pp.oach to building control charts for autocorrelatd data is to a, pp.y classical SPC to the residuals from a time series model fitted. Unfortunately, one cannot completely escape the effects of autocorrelation by using charts based on residuals of time series model. For the detection of variance change in the process we propose a CUSUM of squares control chart which does not require the model identification. The proposed CUSUM of squares chart and the conventional control charts are compared by a Monte Carlo simulation. It is shown that the CUSUM of squares chart is more effective in the presence of dependency in the processes.

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An Economic Design of the EWMA Control Charts with Variable Sampling Interval (VSI EWIMA 관리도의 경제적 설계)

  • 송서일;정혜진
    • Journal of Korean Society for Quality Management
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    • v.30 no.4
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    • pp.1-14
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    • 2002
  • Traditional SPC techniques are looking out variation of process by fixed sampling interval and fixed sample size about every hour, the process of in-control or out-of-control couldn't be detected actually when the sample points are plotted near control limits, and it takes no notice of expense concerned with such sample points. In this paper, to overcome that, consider VSI(variable sampling interval) EWMA control charts which VSI method is applied. The VSI control charts use a short sampling internal if previous sample points are plotted near control limits, then the process has high probability of out-of-control. But it uses a long sampling interval if they are plotted near centerline of the control chart, since process has high possibility of in-control. And then a comparison and analysis between FSI(fixed sampling interval) and VSI EWMA in the statistical aspect and economic aspect is studied. Finally, we show that VSI EWMA control chart is more efficient than FSI EWMA control chart in the both aspects.

A Robust EWMA Control Chart (로버스트 지수가중 이동평균(EWMA) 관리도)

  • Nam, Ho-Soo;Lee, Byung-Gun;Joo, Cheol-Min
    • Journal of the Korean Data and Information Science Society
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    • v.10 no.1
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    • pp.233-241
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    • 1999
  • Control chart is a very extensively used tool in testing whether a process is in a state of statistical control or not. In this paper, we propose a robust EWMA(exponentially weighted moving averages) control chart for variables, which is based on the Huber's M-estimator. The Huber's M-estimator is a well-known robust estimator in sense of distributional robustness. In the proposed chart, the estimation of the process deviation is modified to have a s table level and high power. To compare the performances of the proposed control chart with other charts, some Monte Carlo simulations we performed. The simulation results show that the robust EWMA control chart has good performance.

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Model Parameter Based Fault Detection for Time-series Data (시계열을 따르는 공정데이터의 모델 모수기반 이상탐지)

  • Park, Si-Jeo;Park, Cheong-Sool;Kim, Sung-Shick;Baek, Jun-Geol
    • Journal of the Korea Society for Simulation
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    • v.20 no.4
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    • pp.67-79
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    • 2011
  • The statistical process control (SPC) assumes that observations follow the particular statistical distribution and they are independent to each other. However, the time-series data do not always follow the particular distribution, and most of cases are autocorrelated, therefore, it has limit to adopt the general SPC in tim series process. In this study, we propose a MPBC (Model Parameter Based Control-chart) method for fault detection in time-series processes. The MPBC builds up the process as a time-series model, and it can determine the faults by detecting changes parameters in the model. The process we analyze in the study assumes that the data follow the ARMA (p,q) model. The MPBC estimates model parameters using RLS (Recursive Least Square), and $K^2$-control chart is used for detecting out-of control process. The results of simulations support the idea that our proposed method performs better in time-series process.

Economic-Statistical Design of VSSI Cause-Selecting Charts Considering Two Assignable Causes (두 개의 이상원인을 고려한 VSSI 원인선별 관리도의 경제적-통계적 설계)

  • Jung, Min-Su;Lim, Tae-Jin
    • Journal of Korean Society for Quality Management
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    • v.37 no.1
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    • pp.29-39
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    • 2009
  • This article investigates economic-statistical design of VSSI(variable sampling size and interval) cause-selecting charts considering two assignable causes. We consider a process which is composed of two dependent sub-processes. In each sub-process, two kinds of assignable cause may exist. We propose a procedure for designing VSSI cause-selecting charts, based on Lorenzen and Vance model. Computational experiments show that the VSSI cause-selecting chart is superior to the FSSI cause-selecting chart in the economic-statistical characteristics, even under two assignable causes.

INFLUENCE OF SPECIAL CAUSES ON STOCHASTIC PROCESS ADJUSTMENT

  • Lee, Jae-June;Mihye Ahn
    • Journal of the Korean Statistical Society
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    • v.33 no.2
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    • pp.219-231
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    • 2004
  • Process adjustment is a complimentary tool to process monitoring in process control. Although original intention of process adjustment is not identifying a special cause, detection and elimination of special causes may lead to significant process improvement. In this paper, we examine the impact of special causes on process adjustment. The bias in the adjusted output process is derived for each type of special causes, and average run length (ARL) of the Shewhart chart applied to the adjusted output is computed for each special cause types. Numerical results are illustrated for the ARL of the Shewhart chart, thereupon seriousness of special causes on process adjustment is evaluated for each type of special causes.